QA Engineering Lead, Product Quality Assurance
Meta London, England, United Kingdom
Software Development · 10,001+ employees
About the role
Define and own the product quality strategy across multiple product organizations while leading cross-functional programs to ensure reliability and performance. Partner with engineering and infrastructure teams to scale automation frameworks and drive organization-wide adoption of improved quality practices.
What they look for
Requirements
Requires 6+ years of experience in quality or test engineering across consumer or business software products. Candidates must demonstrate expertise in test automation, non-functional testing, and leading quality strategies across distributed teams.
Full description
Meta is seeking an experienced QA Engineering Lead to drive product quality strategy across our consumer and business product portfolio, including Instagram, Facebook, Meta AI, WhatsApp, Ads, and Privacy. In this role, you will define and execute quality programs that span multiple engineering organizations and geographies, ensuring the reliability, correctness, and performance of products used by billions of people worldwide. You will partner closely with engineering, product, and infrastructure leaders to embed quality practices at scale, influence high-impact decisions, and raise the bar on test engineering across the organization.
Responsibilities
- Define and own the product quality strategy across one or more product organizations, setting direction for test planning, automation, and quality standards
- Lead cross-functional quality programs spanning multiple products, teams, and geographic locations, driving alignment across engineering and product stakeholders
- Establish the technical direction for test strategy on high-ambiguity, business-critical initiatives to ensure product correctness and reliability at launch
- Partner with engineering and infrastructure teams to design and scale automation frameworks that prevent regressions and support long-term product reliability
- Identify systemic quality gaps and drive organization-wide adoption of improved processes, tooling, and quality-driven engineering practices
- Define and track quality metrics that measure test effectiveness, coverage, and overall product health across the portfolio
- Assess non-functional quality dimensions including accessibility, internationalization, localization, performance, and stress testing in partnership with specialized teams
- Advise and influence engineering leaders and cross-functional partners on quality tradeoffs, risk mitigation, and launch readiness decisions
- Mentor and develop quality and test engineers, sharing best practices and elevating quality standards across the organization
- Leverage data and testing insights to inform product decisions and drive measurable improvements in quality outcomes
Minimum Qualifications
- 6+ years of quality engineering or test engineering experience across consumer or business software products
- Experience leading quality strategies and programs across multiple teams and geographic locations, including distributed and offshore teams
- Experience with QA methodologies including test planning, test design, execution, and non-functional testing such as performance and stress testing
- Experience implementing and maintaining test automation frameworks for mobile and web applications
- Experience coding in Python, Java, PHP, C/C++, or an equivalent programming language
Preferred Qualifications
- Experience working across globally distributed engineering teams spanning multiple time zones and product domains
- Experience integrating AI tools into quality workflows to improve test coverage, efficiency, or defect detection
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Experience driving organization-wide quality initiatives and influencing engineering organizations at large scale
- Experience communicating quality strategy and risk assessments to technical and non-technical stakeholders, including executive audiences
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
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